Forecasting Analysis for Glina Arts Fall 2016 Enrollment
This forecasting project for Glina Arts involves predicting total student enrollment for Fall 2016 based on different factors like media and courses. Assumptions were made regarding data increments and season changes. Single Exponential Smoothing technique was used to make short-term forecasts. Statistical formulae such as Mean Absolute Error and Mean Square Error were applied for error measurement. The exponential smoothing formula and trend analysis were employed to forecast student enrollment accurately.
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FORECASTING FOR GLINA ARTS Asha Gangineni Samyukta Varre Gowtham Laburam Rahul Sawant
Problem statement Prediction of Total enrollment of students for Fall -2016 Prediction of Enrollment of students by media for Fall-2016 Prediction of enrollment of Students by Courses for Fall-2016
Assumptions We incremented the Data by one year, EG: Fall 2014 became Fall 2015 Data is available till Spring 2015 , so we incremented the data by one year such that the prediction can be made for fall 2016 No Specified Season was changed to Fall 2014 Registration count <5 is not considered 2D and No media Specified were not considered as data was not available for 5 seasons
Approach and Technology Considered Single Exponential Smoothing: Single Exponential Smoothing: Suitable when there is no trend or seasonal pattern Forecast for a small period of time.
Selecting the Target Element Tr Trend Line For Months Trend Line For Quarters R^2 determines the strength of relationship( values are between 0 and 1) Closer to 1 then there is a good relationship between the factors that we are comparing.
Statistical Formulae Used Mean Absolute Error: Mean Absolute Error: The MAE measures the average magnitude of the errors in a set of forecast. Mean Absolute Percentage Error: Mean Absolute Percentage Error: Mean Absolute Percent Error (MAPE) is the most common measure of forecast error. The MAPE (Mean Absolute Percent Error) measures the size of the error in percentage terms Mean Square Error: Mean Square Error: The Mean Squared Error (MSE) is a measure of how close a fitted line is to data points
Statistical Formulae Used Forecasting Exponential Smoothing Ft+1 = At +(1- ) Ft where = Smoothing Factor At = Last Actual value Ft = Last Forecast Value
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Recommendations No sufficient data , Missing links in data Better forecasting could be done id data was available for more than 4 years Maintain two different columns for Registration and enrollment dates for more accurate prediction Provide recent data
